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Biological Sequence

The Experts below are selected from a list of 31527 Experts worldwide ranked by ideXlab platform

C Mears – 1st expert on this subject based on the ideXlab platform

  • a simple statistical algorithm for Biological Sequence compression
    Data Compression Conference, 2007
    Co-Authors: Lloyd Allison, C Mears

    Abstract:

    This paper introduces a novel algorithm for Biological Sequence compression that makes use of both statistical properties and repetition within Sequences. A panel of experts is maintained to estimate the probability distribution of the next symbol in the Sequence to be encoded. Expert probabilities are combined to obtain the final distribution. The resulting information Sequence provides insight for further study of the Biological Sequence. Each symbol is then encoded by arithmetic coding. Experiments show that our algorithm outperforms existing compressors on typical DNA and protein Sequence datasets while maintaining a practical running time

  • DCC – A Simple Statistical Algorithm for Biological Sequence Compression
    2007 Data Compression Conference (DCC'07), 2007
    Co-Authors: Lloyd Allison, C Mears

    Abstract:

    This paper introduces a novel algorithm for Biological Sequence compression that makes use of both statistical properties and repetition within Sequences. A panel of experts is maintained to estimate the probability distribution of the next symbol in the Sequence to be encoded. Expert probabilities are combined to obtain the final distribution. The resulting information Sequence provides insight for further study of the Biological Sequence. Each symbol is then encoded by arithmetic coding. Experiments show that our algorithm outperforms existing compressors on typical DNA and protein Sequence datasets while maintaining a practical running time

Dominique Lavenier – 2nd expert on this subject based on the ideXlab platform

  • samba hardware accelerator for Biological Sequence comparison
    Bioinformatics, 1997
    Co-Authors: Pascale Guerdouxjamet, Dominique Lavenier

    Abstract:

    Motivation: SAMBA (Systolic Accelerator for Molecular Biological Applications) is a 128 processor hardware accelerator for speeding up the Sequence comparison process. The short-term objective is to provide a low-cost board to boost PC or workstation performance on this class of applications. This paper places SAMBA amongst other existing systems and highlights the original features. Results: Real performance obtained from the prototype is demonstrated. For example, a Sequence of 300 amino acids is scanned against SWISS-PROT-34 (21210389 residues) in 30 s using the Smith and Waterman algorithm. More time-consuming applications, like the bank-to-bank comparison, are computed in a few hours instead of days on standard workstations. Technology allows the prototype to fit onto a single PCI board for plugging into any PC or workstation. Availability: SAMBA can be tested on the WEB server at URL http://www.irisa.fr/SAMBA/

  • SAMBA: hardware accelerator for Biological Sequence comparison.
    Computer applications in the biosciences : CABIOS, 1997
    Co-Authors: P Guerdoux-jamet, Dominique Lavenier

    Abstract:

    SAMBA (Systolic Accelerator for Molecular Biological Applications) is a 128 processor hardware accelerator for speeding up the Sequence comparison process. The short-term objective is to provide a low-cost board to boost PC or workstation performance on this class of applications. This paper places SAMBA amongst other existing systems and highlights the original features.

  • Dedicated Hardware for Biological Sequence Comparison.
    Journal of Universal Computer Science, 1996
    Co-Authors: Dominique Lavenier

    Abstract:

    Biological Sequence comparison is a time consuming task on a Von Neuman computer. The addition of dedicated hardware for parallelizing the comparison algorithms results in a reduction of several orders of magnitude in the execution time. This paper presents and compares different dedicated approaches, based on the parallelization of the algorithms on linear arrays of processors.

Alba Cristina Magalhaes Alves De Melo – 3rd expert on this subject based on the ideXlab platform

  • Using Multiple Fickett Bands to Accelerate Biological Sequence Comparisons.
    Journal of Computational Biology, 2019
    Co-Authors: G. G. Silva, Edans F. De O. Sandes, George Teodoro, Alba Cristina Magalhaes Alves De Melo

    Abstract:

    Abstract Most of the exact algorithms for Biological Sequence comparison obtain the optimal result by calculating dynamic programming (DP) matrices with quadratic time and space complexity. Fickett…

  • Formalization of block pruning: reducing the number of cells computed in exact Biological Sequence comparison algorithms
    The Computer Journal, 2017
    Co-Authors: Edans F. De O. Sandes, George Teodoro, Eduard Ayguadé, Xavier Martorell, Maria Emilia M. T. Walter, Alba Cristina Magalhaes Alves De Melo

    Abstract:

    This is a pre-copyedited, author-produced version of an article accepted for publication in The Computer Journal following peer review. The version of record Edans F O Sandes, George L M Teodoro, Maria Emilia M T Walter, Xavier Martorell, Eduard Ayguade, Alba C M A Melo; Formalization of Block Pruning: Reducing the Number of Cells Computed in Exact Biological Sequence Comparison Algorithms, The Computer Journal, Volume 61, Issue 5, 1 May 2018, Pages 687–713 is available online at: The Computer Journal https://academic.oup.com/comjnl/article-abstract/61/5/687/4539903 and https://doi.org/10.1093/comjnl/bxx090.

  • parallel optimal pairwise Biological Sequence comparison algorithms platforms and classification
    ACM Computing Surveys, 2016
    Co-Authors: Edans F. De O. Sandes, Azzedine Boukerche, Alba Cristina Magalhaes Alves De Melo

    Abstract:

    Many bioinformatics applications, such as the optimal pairwise Biological Sequence comparison, demand a great quantity of computing resource, thus are excellent candidates to run in high-performance computing (HPC) platforms. In the last two decades, a large number of HPC-based solutions were proposed for this problem that run in different platforms, targeting different types of comparisons with slightly different algorithms and making the comparative analysis of these approaches very difficult. This article proposes a classification of parallel optimal pairwise Sequence comparison solutions, in order to highlight their main characteristics in a unified way. We then discuss several HPC-based solutions, including clusters of multicores and accelerators such as Cell Broadband Engines (CellBEs), Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and Intel Xeon Phi, as well as hybrid solutions, which combine two or more platforms, providing the actual landscape of the main proposals in this area. Finally, we present open questions and perspectives in this research field.